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Predicting Strategies for Lead Optimization Via Learning to Rank

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Predicting Strategies for Lead Optimization Via Learning to Rank IPSJ Transactions on Bioinformatics Vol.11 41–47 (Dec. 2018) [DOI: 10.2197/ipsjtbio.11.41] Original Paper Predicting Strategies for Lead Optimization via Learning to Rank Nobuaki Yasuo1,2,a) Keisuke Watanabe1 Hideto Hara3 Kentaro Rikimaru3 Masakazu Sekijima1,4,b) Received: August 21, 2018, Accepted: September 18, 2018 Abstract: Lead optimization is an essential step in drug discovery in which the chemical structures of compounds are modified to improve characteristics such as binding affinity, target selectivity, physicochemical properties, and tox- icity. We present a concept for a computational compound optimization system that outputs optimized compounds from hit compounds by using previous lead optimization data from a pharmaceutical company. In this study, to predict the drug-likeness of compounds in the evaluation function of this system, we evaluated and compared the ability to correctly predict lead optimization strategies through learning to rank methods. Keywords: lead optimization, learning to rank, computer-aided drug design, machine learning computer-aided drug discovery (CADD), which has been utilized 1. Introduction since the 1960s, are also leading current drug discovery. The During drug discovery, enormous attempts are being made to methods of CADD can be combined with various biological data identify better drug candidates. Since the cost of drug discovery including genomic sequence, protein tertiary structure, and chem- has been drastically increased, recently the process of drug dis- ical structure, and can be utilized in various steps in drug discov- covery typically takes 12–14 years [1] and costs approximately ery: target identification, compound screening, and ADME (ab- 2.6 billion USD [2]. The process of drug discovery is sometimes sorption, distribution, metabolism, excretion, toxicity) properties likened to looking for a needle in a haystack; it is the process prediction [9], [10], [11]. To this end, methods in CADD such as of finding out suitable compounds from vast “chemical space.” virtual screening, have been widely applied in drug discovery to First, compounds are screened on the basis of their binding affin- reduce experimental costs [12], [13]. It is expected that CADD ity to a target protein to obtain hit compounds. Then, in hit-to- reduces the cost of drug development by 50% [14]. lead and lead optimization steps, these hits are optimized to ob- Nearly all of the cost of lead optimization originates from the tain drug candidates. Subsequently, the optimized compounds are synthesis of many compounds in an effort to explore the entire designated for preclinical and clinical testing. Compounds that chemical space, but this exploration typically results in only a pass these tests are finally approved as drugs. Lead optimization, few, or if any, potential candidates. A discovery strategy that in which the chemical structures of lead compounds are modi- minimizes the number of compounds synthesized would greatly fied to obtain with improved properties, is an essential step in improve the efficiency of candidate development, since 17% of drug discovery [3], [4]. Properties such as binding affinity, se- total drug discovery cost were invested for lead optimization [1]. lectivity, physicochemical and ADMET (absorption, distribution, However, researches on lead optimization are limited since prac- metabolism, excretion, toxicity) properties are optimized in the tical data of lead optimization have not been published from phar- hit-to-lead and lead optimization steps [5], [6] (Fig. 1). maceutical companies. In order to reduce the cost of these processes, diverse ap- The ultimate research objective in this study was to develop proaches have been developed. Combinatorial chemistry and an in silico compound optimization system to produce optimized high-throughput screening are the key technologies to acceler- compounds from hit compounds (Fig. 2). In this system, two ate the drug discovery from experimental biology [7], [8], while modules are iteratively applied. The first module focuses on the exploration of candidate compounds, and the second evalu- 1 Department of Computer Science, Tokyo Institute of Technology, Yoko- ates the identified candidates. The exploration module is based hama, Kanagawa 226–8503, Japan 2 Research Fellow of Japan Society for the Promotion of Science DC1, on virtual modification of compounds by using matched molecu- Yokohama, Kanagawa 226–8503, Japan lar pairs (MMPs) or chemical reaction-based method. An MMP 3 Shonan Research Center, Takeda Pharmaceutical Company Limited, is a pair of compounds that differing in only in one part of Fujisawa, Kanagawa 251–0012, Japan 4 Advanced Computational Drug Discovery Unit, Tokyo Institute of Tech- their chemical structure [16], and MMPs have previously been nology, Yokohama, Kanagawa 226–8503, Japan used for ADME prediction [17] and compound optimization [18]. a) [email protected] Chemical reaction-based method simulates virtual chemical re- b) [email protected] c 2018 Information Processing Society of Japan 41 IPSJ Transactions on Bioinformatics Vol.11 41–47 (Dec. 2018) Fig. 1 Scheme of lead optimization. The hit compound is optimized through step-wise exploration. In each step, new compounds are synthesized and evaluated. Compounds with unfavorable proper- ties are pruned and will not be explored in further steps. In this figure, binding affinity towards the target protein is used as an example of the evaluation function. Fig. 2 Concept of an in silico compound optimization system. The system learn optimization strategies from previous lead optimization projects. When new hit compound are input, the two modules are iteratively applied: exploration module and evaluation module. In exploration module, modified compounds are virtually explored from input compounds using virtual compound optimization system [15]. In evaluation module, input compounds are evaluated by learned strategy. actions to generate new compounds, and have previously been of compounds. Machine-learning-based approaches such as used for compound optimization [15]. As this method uses practi- support vector machine (SVM) and neural network (NN) have cal chemical reactions for exploration, generated compounds are also been applied to distinguish drug-like from non drug-like more synthesizable than MMP-based systems. compounds [26], [27], [28]. However, these methods remain In contrast, quantitative structure-activity relationships inadequate because they were originally developed only to (QSARs) [19], [20] and quantitative structure-property relation- distinguish drugs from non-drugs. Consequently, there is a ships (QSPRs) [21], [22] have been widely used to evaluate high demand for new methods that can predict drug-likeness compounds. However, such methods permit the simultaneous of compounds that have been gradually optimized during lead comparison of only a limited number of properties. Various optimization. physicochemical-property-based metrics or substructure- Learning to rank is a machine learning method that is well based drug-likeness indices, such as solubility [23], Lipinski’s suited for addressing this issue. Figure 3 shows the idea of learn- rule of five [24], and quantitative estimate of drug-likeness ing to rank method. This method, which has been developed (QED) [25], have been developed to assess the drug-likeness in the field of information retrieval, predicts the order of a set c 2018 Information Processing Society of Japan 42 IPSJ Transactions on Bioinformatics Vol.11 41–47 (Dec. 2018) Fig. 3 Idea of pairwise learning to rank method. In learning phase, the pair of data and the relationship are input as the training data. In inference phase, test data are sorted using learned relationship. of data [29] rather than the class or specific value of each data. likely to be drug-like than compounds that synthesized earlier. Learning to rank methods have previously been applied to vir- All compounds were numbered in time order, and the assigned tual screening, where the accuracies outperformed simple SVM numbers were normalized before use. For the pairwise methods, and support vector regression (SVR) [30], [31]. Learning to rank only pairs from the same project were used for training. methods can be categorized as pointwise, pairwise, and listwise methods. In pointwise methods, a value is assigned to each data 2.2 Features point, and those value are sorted to determine the order. In pair- All compounds in the dataset were encoded as feature vec- wise methods, the ordering is first determined for pairs of data tors. In this study, the feature vector consisted of Extended- points, and the ordered pairs are then sorted to determine the fi- connectivity fingerprint (ECFP) [32], which is topological finger- nal order as in Fig. 3 In listwise methods, the order of a dataset print for molecular characterization. The algorithm of ECFP fin- is directly predicted. Among these methods, listwise method is gerprint is described in Fig. 4, using 4-methyloxazole as an ex- not suited for this task since large datasets are required to train ample. Substructures starting from each atom are iteratively com- listwise method. bined to the neighbor atoms until the diameters of substructures In this study, we propose a strategy for lead optimization based reach specified number. Each of the constructed substructures on compounds that have previously been synthesized at Takeda corresponds to one bit of the fixed-length feature vector by us- Pharmaceutical Company Limited. In this strategy, we predicted
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